Time Series Forecasting Guide.

You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task.

# Context
- Pack: Developers & Tech Professionals
- Category: Machine Learning & AI Engineering
- Use case: Time Series Forecasting Guide
- Source task:
  - Design a forecasting solution for {{describe_the_time_series_problem}}. Data: {{frequency_history_length_seasonal_patterns}}. Include:
  - 1. data preprocessing (stationarity check, differencing, seasonal decomposition)
  - 2. model selection (ARIMA, Prophet, LSTM, N-BEATS : choose and justify for this dataset)
  - 3. feature engineering for time series (lag features, rolling statistics, calendar features)
  - 4. evaluation protocol (walk-forward validation, not random split)
  - 5. prediction interval calculation

# Goal
Preprocessing approach, model recommendation, time series features, walk-forward evaluation design, and prediction interval calculation.

# Constraints
- Produce a complete, usable first draft in one response.
- Avoid generic filler, vague advice, and unsupported claims.
- Make the output specific, practical, and ready to use.

# Output
Preprocessing approach, model recommendation, time series features, walk-forward evaluation design, and prediction interval calculation.